Work

Robust Brain Tumour Classification: Comparative Deep Learning Analysis with Ensemble Modelling

Year: 2025

Type: conference-paper

Abstract: Accurate detection of brain tumours from MRI scans is essential for early diagnosis and treatment. Traditional approaches, including manual analysis by radiologists and classical machine learning methods relying on handcrafted features, often lack consistency and high accuracy. This study explores VGG16, VGG19, DenseNet121, ResNet50, Ensemble model (VGG16 + DenseNet121), MobileNetV2, and NASNet for automated brain tumour detection. Using the Brain Tumour Classification (MRI) dataset, VGG16 and DenseNet121 achieved the highest accuracy of 94.08%, demonstrating the effectiveness of transfer learning. An ensemble model was also used combing the 2 best models VGG16 and Densenet121 to create a better generalized model with ROC-AUC value of 0.9960. The findings emphasize CNNs' potential in enhancing the efficiency and precision of brain tumour diagnosis.

Source: EPJ Web of Conferences

DOI: 10.1051/epjconf/202532801033

Authors: Aditya Verma, Disha Wankhede

Institution: Vishwakarma Institute of Technology

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4411430622